
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			UpperCAmelCase_     (					_A    ,			_A	):


  '''simple docstring'''







  @register_to_config
  def __init__(					self     :					Dict     ,				*,
      UpperCamelCase__     :					int = 4     ,				UpperCamelCase__     :					int = 768     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[Any]     ,				)     ->      Optional[Any]:







    """simple docstring"""






    super().__init__()

    __magic_name__								=			nn.Parameter(torch.zeros(UpperCamelCase__       )       )

    # parameters for additional clip time embeddings
    __magic_name__								=			nn.Linear(UpperCamelCase__     ,				UpperCamelCase__       )
    __magic_name__								=			nn.Linear(UpperCamelCase__     ,				UpperCamelCase__       )

    # parameters for encoder hidden states
    __magic_name__								=			clip_extra_context_tokens
    __magic_name__								=			nn.Linear(
        UpperCamelCase__     ,				self.clip_extra_context_tokens * cross_attention_dim       )
    __magic_name__								=			nn.Linear(UpperCamelCase__     ,				UpperCamelCase__       )
    __magic_name__								=			nn.LayerNorm(UpperCamelCase__       )





  def 				_lowercase				(					self     :					str     ,				*, UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int       )     ->      List[Any]:







    """simple docstring"""






    if do_classifier_free_guidance:
      # Add the classifier free guidance embeddings to the image embeddings
      __magic_name__								=			image_embeddings.shape[0]
      __magic_name__								=			self.learned_classifier_free_guidance_embeddings.unsqueeze(0       )
      __magic_name__								=			classifier_free_guidance_embeddings.expand(
          UpperCamelCase__     ,				-1       )
      __magic_name__								=			torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,				dim=0       )

    # The image embeddings batch size and the text embeddings batch size are equal
    assert image_embeddings.shape[0] == prompt_embeds.shape[0]

    __magic_name__								=			prompt_embeds.shape[0]

    # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
    # adding CLIP embeddings to the existing timestep embedding, ...
    __magic_name__								=			self.embedding_proj(UpperCamelCase__       )
    __magic_name__								=			self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__       )
    __magic_name__								=			time_projected_image_embeddings + time_projected_prompt_embeds

    # ... and by projecting CLIP embeddings into four
    # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
    __magic_name__								=			self.clip_extra_context_tokens_proj(UpperCamelCase__       )
    __magic_name__								=			clip_extra_context_tokens.reshape(UpperCamelCase__     ,				-1     ,				self.clip_extra_context_tokens       )
    __magic_name__								=			clip_extra_context_tokens.permute(0     ,				2     ,				1       )

    __magic_name__								=			self.encoder_hidden_states_proj(UpperCamelCase__       )
    __magic_name__								=			self.text_encoder_hidden_states_norm(UpperCamelCase__       )
    __magic_name__								=			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,				dim=1       )

    return text_encoder_hidden_states, additive_clip_time_embeddings



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


__lowerCAmelCase     :	Optional[Any]       			=  'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			_ask_options(
      """In which compute environment are you running?""",	["""This machine""", """AWS (Amazon SageMaker)"""],	_convert_compute_environment,	)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    __magic_name__								=			get_sagemaker_input()
  else:
    __magic_name__								=			get_cluster_input()
  return config

def a__       (			A_=None   ):






  '''simple docstring'''





  if subparsers is not None:
    __magic_name__								=			subparsers.add_parser("""config""",	description=A_   )
  else:
    __magic_name__								=			argparse.ArgumentParser("""Accelerate config command""",	description=A_   )

  parser.add_argument(
      """--config_file""",	default=A_,	help=(
          """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      ),	)

  if subparsers is not None:
    parser.set_defaults(func=A_   )
  return parser

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			get_user_input()
  if args.config_file is not None:
    __magic_name__								=			args.config_file
  else:
    if not os.path.isdir(A_   ):
      os.makedirs(A_   )
    __magic_name__								=			default_yaml_config_file

  if config_file.endswith(""".json"""   ):
    config.to_json_file(A_   )
  else:
    config.to_yaml_file(A_   )
  print(f'''accelerate configuration saved at {config_file}'''   )

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			config_command_parser()
  __magic_name__								=			parser.parse_args()
  config_command(A_   )


if __name__ == "__main__":
    main()

